MétaCan
Menu
Back to cohort
Record W2587420003 · doi:10.1093/annonc/mdw611

Afatinib versus gefitinib in patients with EGFR mutation-positive advanced non-small-cell lung cancer: overall survival data from the phase IIb LUX-Lung 7 trial

2017· article· en· W2587420003 on OpenAlexaff
Luis Paz‐Ares, E.H. Tan, Kenneth J. O’Byrne, L. Zhang, Vera Hirsh, Michael Boyer, James Chih‐Hsin Yang, Tony Mok, K.H. Lee, Shun Lü, Yajun Shi, D. H. Lee, Janessa Laskin, D.-W. Kim, Scott A. Laurie, Karl Kölbeck, Jean Fan, Nigel Dodd, Angela Märten, K. Park

Bibliographic record

VenueAnnals of Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsBC Cancer AgencyOttawa HospitalMcGill University
FundersChugai PharmaceuticalGenentechOno PharmaceuticalAstellas PharmaChinese University of Hong KongGlaxoSmithKlineAstraZeneca KoreaClovis OncologyVertex PharmaceuticalsInternational Association for the Study of Lung CancerAmgenACEA BiosciencesPfizerCelgeneAstraZenecaEli Lilly and Company
KeywordsGefitinibAfatinibMedicineInternal medicineLung cancerHazard ratioOncologyEpidermal growth factor receptorT790MProportional hazards modelGastroenterologyConfidence intervalCancer

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.457
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations522
Published2017
Admission routes1
Has abstractno

Explore more

Same venueAnnals of OncologySame topicLung Cancer Treatments and MutationsFrench-language works237,207